| /third_party/boost/libs/parameter/test/literate/ |
| D | predicate-requirements0.cpp | 4 BOOST_PARAMETER_NAME((_graph, graphs) graph)
5 BOOST_PARAMETER_NAME((_visitor, graphs) visitor)
6 BOOST_PARAMETER_NAME((_root_vertex, graphs) in(root_vertex))
7 BOOST_PARAMETER_NAME((_index_map, graphs) in(index_map))
8 BOOST_PARAMETER_NAME((_color_map, graphs) in_out(color_map))
27 , graphs::graph
76 , graphs::graph
103 , graphs::graph
140 BOOST_PARAMETER_FUNCTION((void), depth_first_search, graphs,
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| /third_party/mindspore/tests/ut/cpp/cxx_api/ |
| D | serialization_test.cc | 88 std::vector<Graph> graphs; in TEST_F() local
90 ModelType::kMindIR, &graphs) == kSuccess); in TEST_F()
94 std::vector<Graph> graphs; in TEST_F() local
96 ModelType::kMindIR, &graphs) == kSuccess); in TEST_F()
100 std::vector<Graph> graphs; in TEST_F() local
102 ModelType::kMindIR, &graphs); in TEST_F()
109 std::vector<Graph> graphs; in TEST_F() local
111 …:string>(2, "./data/mindir/add_encrpty_key_0123456789ABCDEF.mindir"), ModelType::kMindIR, &graphs); in TEST_F()
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| /third_party/mindspore/mindspore/ccsrc/runtime/framework/ |
| D | control_node_parser.h | 90 …void Parse(const std::vector<AnfNodePtr> &control_nodes, const std::vector<KernelGraphPtr> &graphs,
144 …tValueNode(const std::vector<AnfNodePtr> &control_nodes, const std::vector<KernelGraphPtr> &graphs,
154 void FetchFrontToBackendParameter(const std::vector<KernelGraphPtr> &graphs,
159 void FetchFrontToBackendKernel(const std::vector<KernelGraphPtr> &graphs,
180 void FetchCallInputKernelGraph(const std::vector<KernelGraphPtr> &graphs,
183 void FetchBackendInputNode(const std::vector<KernelGraphPtr> &graphs,
188 void FetchBackendParameterNode(const std::vector<KernelGraphPtr> &graphs,
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| D | control_node_parser.cc | 722 …ser::Parse(const std::vector<AnfNodePtr> &control_nodes, const std::vector<KernelGraphPtr> &graphs, in Parse() argument
724 if (graphs.size() != device_contexts.size()) { in Parse()
725 MS_LOG(EXCEPTION) << "Graph num is not equal to device context, graph:" << graphs.size() in Parse()
728 if (graphs.empty()) { in Parse()
748 FetchFrontToBackendParameter(graphs, device_contexts, real_to_formal_front_parameters, in Parse()
755 FetchCallInputKernelGraph(graphs, device_contexts); in Parse()
757 FetchFrontValueNode(control_nodes, graphs, device_contexts); in Parse()
759 FetchFrontToBackendKernel(graphs, device_contexts); in Parse()
761 FetchCallInputKernelGraph(graphs, device_contexts); in Parse()
767 …FetchBackendInputNode(graphs, device_contexts, real_to_formal_front_parameters, formal_to_real_fro… in Parse()
[all …]
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| D | graph_compiler.h | 59 …GraphCompilerInfo(const std::vector<KernelGraphPtr> &graphs, const std::vector<DeviceContext *> &d… in GraphCompilerInfo()
66 : graphs_(graphs), in GraphCompilerInfo()
165 void Summary(const std::vector<KernelGraphPtr> &graphs) const;
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| /third_party/boost/libs/parameter/test/ |
| D | tutorial.cpp | 8 namespace graphs { namespace
19 namespace graphs { namespace core { namespace
34 using namespace graphs; in main()
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| /third_party/mindspore/mindspore/_extends/graph_kernel/ |
| D | parallel_estimate.py | 28 graphs = []
30 graphs.append(model.load_composite(gd).graph)
31 estimation = model.parallel_estimate(graphs)
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| /third_party/mindspore/tests/st/graph_kernel/model/ |
| D | test_split.py | 39 graphs = sp.resolve_connnected_graphs(nodes)
40 if len(graphs) != 1:
351 graphs = sp.resolve_connnected_graphs(n1)
352 print(graphs)
353 assert len(graphs) == 1
355 graphs = sp.resolve_connnected_graphs(n2)
356 print(graphs)
357 assert len(graphs) == 2
359 graphs = sp.resolve_connnected_graphs(n3)
360 print(graphs)
[all …]
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| D | test_graph_parallel.py | 36 def block_fusion(graphs): argument
37 gain = model.parallel_estimate(graphs)
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| /third_party/mindspore/mindspore/ccsrc/runtime/framework/actor/ |
| D | debug_actor.cc | 76 void DebugActor::DebugOnStepBegin(std::vector<KernelGraphPtr> graphs, std::vector<DeviceContext *> … in DebugOnStepBegin() argument
83 debugger->PreExecuteGraphDebugger(graphs); in DebugOnStepBegin()
90 for (size_t i = 0; i < graphs.size(); ++i) { in DebugOnStepBegin()
93 DumpJsonParser::GetInstance().SaveGraph(graphs[i].get()); in DebugOnStepBegin()
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| /third_party/mindspore/include/api/ |
| D | serialization.h | 67 …tatus Load(const std::vector<std::string> &files, ModelType model_type, std::vector<Graph> *graphs,
81 …Load(const std::vector<std::vector<char>> &files, ModelType model_type, std::vector<Graph> *graphs,
95 …tion::Load(const std::vector<std::string> &files, ModelType model_type, std::vector<Graph> *graphs, in Load() argument
97 return Load(VectorStringToChar(files), model_type, graphs, dec_key, StringToChar(dec_mode)); in Load()
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| /third_party/mindspore/tests/ut/cpp/parallel/ |
| D | step_parallel_test.cc | 368 FuncGraphSet graphs = manager->func_graphs(); in TEST_F() local
369 FuncGraphPtr graph = *graphs.begin(); in TEST_F()
377 FuncGraphSet graphs = manager->func_graphs(); in TEST_F() local
378 FuncGraphPtr graph = *graphs.begin(); in TEST_F()
386 FuncGraphSet graphs = manager->func_graphs(); in TEST_F() local
387 FuncGraphPtr graph = *graphs.begin(); in TEST_F()
405 FuncGraphSet graphs = manager->func_graphs(); in TEST_F() local
406 FuncGraphPtr graph = *graphs.begin(); in TEST_F()
444 FuncGraphSet graphs = manager->func_graphs(); in TEST_F() local
445 FuncGraphPtr graph = *graphs.begin(); in TEST_F()
[all …]
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| /third_party/boost/libs/math/doc/sf/ |
| D | gegenbauer.qbk | 51 [$../graphs/gegenbauer.svg]
101 [$../graphs/gegenbauer_ulp_3.svg]
102 [$../graphs/gegenbauer_ulp_5.svg]
103 [$../graphs/gegenbauer_ulp_9.svg]
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| D | cardinal_b_splines.qbk | 56 [$../graphs/central_b_splines.svg]
57 [$../graphs/central_b_spline_derivatives.svg]
58 [$../graphs/central_b_spline_second_derivatives.svg]
127 [$../graphs/b_spline_ulp_3.svg]
128 [$../graphs/b_spline_ulp_5.svg]
129 [$../graphs/b_spline_ulp_9.svg]
|
| D | hypergeometric.qbk | 166 [? __build_html '''<?dbhtml-include href="'''__base_path__'''/graphs/hypergeometric_1f1/script_incl…
217 [? __build_html '''<?dbhtml-include href="'''__base_path__'''/graphs/hypergeometric_1f1/negative_b_…
218 [?! __build_html [$../graphs/hypergeometric_1f1/negative_b_incalculable.png]]
240 In the following graphs we ran 100000 random test cases over each domain, note that the scatter plo…
241 as otherwise the graphs are both incomprehensible and virtually unplottable (as in sudden browser d…
246 [$../graphs/hypergeometric_1f1/positive_abz_bins.svg]
247 [? __build_html '''<?dbhtml-include href="'''__base_path__'''/graphs/hypergeometric_1f1/positive_ab…
248 [?! __build_html [$../graphs/hypergeometric_1f1/positive_abz.png]]
252 [$../graphs/hypergeometric_1f1/negative_a_bins.svg]
253 [? __build_html '''<?dbhtml-include href="'''__base_path__'''/graphs/hypergeometric_1f1/negative_a.…
[all …]
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| /third_party/mindspore/mindspore/lite/test/ut/src/runtime/kernel/arm/cxx_api/ |
| D | serialization_test.cc | 38 std::vector<Graph> graphs; in TEST_F() local
40 …Load(std::vector<std::string>(2, "./nets/file_not_exist.mindir"), ModelType::kFlatBuffer, &graphs); in TEST_F()
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| /third_party/boost/libs/math/doc/quadrature/ |
| D | wavelet_transforms.qbk | 36 [$../graphs/wavelet_transform_definition.svg]
40 [$../graphs/daubechies_wavelet_transform_definition.svg]
59 [$../graphs/scalogram_sin1t_light.png]
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| /third_party/boost/libs/graph_parallel/doc/ |
| D | dehne_gotz_min_spanning_tree.rst | 12 graphs. The graphs need not be connected: each algorithm will compute
159 various random graphs. We see that the algorithm scales well up to 64
161 graphs. However, for sparse graphs performance tapers off as the
215 various random graphs. The algorithm only scales well for very dense
216 graphs, where most of the work is performed in the initial stage and
276 various random graphs. We see that the algorithm scales well up to 64
278 graphs. However, for sparse graphs performance tapers off as the
339 various random graphs. We see that the algorithm scales well up to 64
341 graphs. However, for sparse graphs performance tapers off as the
366 consistent performance and scalability for the graphs we
|
| /third_party/mindspore/mindspore/_extends/graph_kernel/model/ |
| D | graph_parallel.py | 272 def block_parallel_estimate(graphs): argument
275 for gid, g in enumerate(graphs):
287 return ParalGain("none", sum_weight, 0, [0 for _ in graphs], None)
293 def parallel_estimate(graphs): argument
295 return block_parallel_estimate(graphs)
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| /third_party/mindspore/mindspore/ccsrc/backend/optimizer/graph_kernel/ |
| D | parallel_cost_model.cc | 51 std::vector<AnfNodePtrList> graphs; in CalFuseInfo() local
52 std::transform(nodes.begin(), nodes.end(), std::back_inserter(graphs), in CalFuseInfo()
55 if (!AnfToJsonDesc(graphs, dump_option, &json_desc)) { in CalFuseInfo()
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| /third_party/boost/libs/graph/doc/ |
| D | read_graphml.rst | 30 The GraphML format can specify both directed and undirected graphs, and
33 the same is true for directed graphs. Furthermore, ``read_graphml``
45 If the file contains multiple graphs, the ``graph_index`` parameter controls
48 number of graphs in the file, an empty graph will be returned.
148 - Nested graphs are supported as long as they are exactly of the same
150 that since nested graphs are not directly supported by BGL, they
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| /third_party/mindspore/tests/ut/cpp/common/ |
| D | backend_common_test.cc | 73 std::vector<FuncGraphPtr> graphs{func_graph}; in GetFuncGraph() local
74 FuncGraphManagerPtr manager = std::make_shared<FuncGraphManager>(graphs); in GetFuncGraph()
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| /third_party/boost/libs/graph/src/ |
| D | graphml.cpp | 124 std::vector< const ptree* > graphs; in run() local
126 get_graphs(gml, desired_idx, true, graphs); in run()
127 BOOST_FOREACH (const ptree* gr, graphs) in run()
148 BOOST_FOREACH (const ptree* gr, graphs) in run()
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| /third_party/mindspore/mindspore/ccsrc/debug/data_dump/ |
| D | cpu_e2e_dump.cc | 173 auto &graphs = DumpJsonParser::GetInstance().graphs(); in DumpParametersAndConst() local
174 for (auto graph : graphs) { in DumpParametersAndConst()
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| /third_party/mindspore/mindspore/core/proto/ |
| D | mind_ir.proto | 42 repeated GraphProto graphs = 13; field
78 repeated GraphProto functions = 8; // all the graphs without the main graph.
|